TY - CHAP

T1 - Which clicks lead to conversions?

T2 - 10th International Conference on E-Business - ICE-B 2013

AU - Nottorf, Florian

N1 - Conference code: 10

PY - 2013

Y1 - 2013

N2 - With an increase in the potential to allocate financial online advertising spending, managers are facing a sophisticated decision and allocation process. We developed a binary logit model with a Bayesian mixture approach to address consumers' buying decision processes and to account for the effects of multiple online advertising channels. By analyzing data from a medium-sized online mail order business, we found inherent differences in the effects of consumer clicks on purchasing probabilities across multiple advertising channels. We developed an alternative approach to account for the different attribution of success of advertising channels - the average success probability (ASP). Compared to standardized metrics, we found paid search advertising to be overestimated and retargeting display advertising to be underestimated. We further found that the mixture approach is useful for considering heterogeneity in the individual propensity of consumers to purchase; for the majority of consumers (more than 90%), repeated clicks on advertisements decrease their probability of purchasing. In contrast with this segment, we found a smaller segment of consumers (nearly 10%) whose clicks on advertisements increase conversion probabilities. Our approaches will help managers to better understand consumer online search and buying behavior over time and to allocate financial spending more efficiently across multiple types of online advertising.

AB - With an increase in the potential to allocate financial online advertising spending, managers are facing a sophisticated decision and allocation process. We developed a binary logit model with a Bayesian mixture approach to address consumers' buying decision processes and to account for the effects of multiple online advertising channels. By analyzing data from a medium-sized online mail order business, we found inherent differences in the effects of consumer clicks on purchasing probabilities across multiple advertising channels. We developed an alternative approach to account for the different attribution of success of advertising channels - the average success probability (ASP). Compared to standardized metrics, we found paid search advertising to be overestimated and retargeting display advertising to be underestimated. We further found that the mixture approach is useful for considering heterogeneity in the individual propensity of consumers to purchase; for the majority of consumers (more than 90%), repeated clicks on advertisements decrease their probability of purchasing. In contrast with this segment, we found a smaller segment of consumers (nearly 10%) whose clicks on advertisements increase conversion probabilities. Our approaches will help managers to better understand consumer online search and buying behavior over time and to allocate financial spending more efficiently across multiple types of online advertising.

KW - Digital media

KW - Bayesian analyis

KW - Clickstream data

KW - Consumer behavior

KW - Mixture of normals

KW - Online advertising

KW - Purchasing probabilities

KW - User-journey

UR - http://www.scopus.com/inward/record.url?scp=84887684654&partnerID=8YFLogxK

M3 - Article in conference proceedings

SN - 978-989-8565-72-3

T3 - ICETE 2013 - 10th Int. Joint Conf. on E-Business and Telecommunications; 4th Int. Conf. DCNET 2013, - 10th Int. Conf. on ICE-B 2013 and OPTICS 2013 - 4th Int. Conf. on Optical Communication Systems

SP - 141

EP - 152

BT - ICE-B 2013 - 10th International Conference on E-Business

A2 - Obaidat, Mohammad S.

PB - Institute for Systems and Technologies of Information, Control and Communication Haskolans (Reykjavik)

Y2 - 29 July 2013 through 31 July 2013

ER -